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Peer-reviewedCC BY 4.0Nature Methods

LoL-align: sensitive and fast probabilistic protein structure alignment

  • Lasse Reifenrath1
  • Michel van Kempen1
  • Gyuri Kim2,3
  • Soo Hyun Kim2,3
  • Mohammadreza Radnezhad1
  • Cameron L. M. Gilchrist2,4
  • Milot Mirdita2,5,6
  • Martin Steinegger2,7,8
  • Johannes Söding1,9
9 affiliations
  1. 1Quantitative and Computational Biology Group, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
  2. 2School of Biological Sciences, Seoul National University, Seoul, South Korea
  3. 3Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
  4. 4Korea Basic Science Institute, Ochang-center, Cheongju, South Korea
  5. 5Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, South Korea
  6. 6Biomedical Institute for Convergence at Sungkyunkwan University, Sungkyunkwan University, Suwon, South Korea
  7. 7Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
  8. 8Institute of Molecular Biology and Genetics, Seoul National University, Seoul, South Korea
  9. 9Campus Institute Data Science (CIDAS), Göttingen, Germany
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Abstract

The ubiquitous availability of protein structures permits replacing sequence alignment with more accurate and sensitive structure alignment algorithms. LoL-align maximizes a local log-odds score for proteins to be homologous, given their intra-protein Cα − Cα distances. Here we show that LoL-align is markedly more sensitive in detecting remote homologs than TM-align and Dali on single- and multidomain benchmarks and achieves better alignment quality at 5–20 × their speed. LoL-align is fully integrated into Foldseek.

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Cite

BibTeXRIS
BibTeX
@article{reifenrath2026lolalign,
  title = {{LoL-align: sensitive and fast probabilistic protein structure alignment}},
  author = {Lasse Reifenrath and Michel van Kempen and Gyuri Kim and Soo Hyun Kim and Mohammadreza Radnezhad and Cameron L. M. Gilchrist and Milot Mirdita and Martin Steinegger and Johannes Söding},
  journal = {Nature Methods},
  year = {2026},
  month = oct,
  doi = {10.1038/s41592-026-03249-w},
  url = {https://doi.org/10.1038/s41592-026-03249-w},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - LoL-align: sensitive and fast probabilistic protein structure alignment
AU  - Lasse Reifenrath
AU  - Michel van Kempen
AU  - Gyuri Kim
AU  - Soo Hyun Kim
AU  - Mohammadreza Radnezhad
AU  - Cameron L. M. Gilchrist
AU  - Milot Mirdita
AU  - Martin Steinegger
AU  - Johannes Söding
PY  - 2026
DA  - 2026/10/14
JO  - Nature Methods
DO  - 10.1038/s41592-026-03249-w
UR  - https://doi.org/10.1038/s41592-026-03249-w
AB  - The ubiquitous availability of protein structures permits replacing sequence alignment with more accurate and sensitive structure alignment algorithms. LoL-align maximizes a local log-odds score for proteins to be homologous, given their intra-protein Cα − Cα distances. Here we show that LoL-align is markedly more sensitive in detecting remote homologs than TM-align and Dali on single- and multidomain benchmarks and achieves better alignment quality at 5–20 × their speed. LoL-align is fully integrated into Foldseek.
SN  - 1548-7091
SN  - 1548-7105
ER  -